GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
AutoAudit leverages LLMs specifically for cyber security tasks and supports custom fine-tuning through models such as GPT, LLAMA, LoRA, and QLORA.
Good fit when
- When your project requires a language model focused on cyber security applications rather than general content generation.
- If you plan to use or adapt existing models like GPT for specialized cyber security tasks with the flexibility of fine-tuning options.
Avoid when
- For projects needing broad, general-purpose text generation that does not require cyber security expertise embedded in the model.
- In scenarios where proprietary data privacy is a concern, given AutoAudit's nature as an LLM for cyber security may imply certain data processing policies could be less flexible.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (568d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/ddzipp/AutoAuditSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
AutoAudit is an LLM tailored for cyber security applications with support for fine-tuning using models like GPT, LLAMA, LoRA, and QLORA.
Capability facts
- Languages
- html
Source: github.language · Sep 19, 2026
Categories
Tags
README
Future Plans 1. Inspired by CyberPal, we plan to synthesize a high quality cybersecurity corpus : This dataset will include open/closed book question answering, yes/no questions, multiple choice Q&A, and Chain of Thoughts (CoT). We aim to open source both the dataset and the corr...
For agents
This page has a .md twin and JSON over the API.